applying stochastic models of geographic evolution to explain species-environment relationships of...

Applying stochastic models of geographic evolution to

explain species-environment relationships of bats in the

New World

J. Sebastián Tello and Richard D. Stevens

Department of Biological SciencesLouisiana State University

Baton Rouge, LA 70803

Variation in species richness at broad geographic extents

Introduction

Bird richnessHawkins et al. 2007

e.g. NPP

Species Richness

Deterministic effects of environmentalconditions is a prominent hypotheses

Introduction

Strong and frequent species-environment correlations

Introduction

Field et al. 2009

Climate

/

Produc

tivity

Heterog

eneity

Nutrients

Prim

acy

adj.

R2

Correlative studies have used simple non-biological null hypotheses

Expected by chance?

Observed Relationship

Environmental variable Environmental variable

Ric

hnes

s

R2=0.513 R2=0.000

Introduction

Richness gradients are formed by the overlap of individual species distributions

Introduction

RichnessGradient

Species Distributions

Species distributions are the consequence of the geographic diversification of clades

Introduction

Species Distributions

Biogeographic Processes and Constraints

a. Confined geographic domain of distribution

b. Aggregated distributions

c. Geographic range movements

d. Cladogenesis:• Speciation• Extinction

Environment assumed to affects richness via fundamental biogeographic processes

Introduction

Distributions

Biogeographic Processes

Richness

Environment

Environment assumed to affects richness via fundamental biogeographic processes

Introduction

Distributions

Biogeographic Processes

Richness

Environment

Biogeographic processes not necessarily driven by environment

Introduction

Distributions

Biogeographic Processes

Richness

Stochasticity

Biogeographic processes not necessarily driven by environment

Introduction

Distributions

Biogeographic Processes

Richness

Environment

Stochasticity

Introduction

Biogeographic Processes

Richness

Environment

Stochasticity

How do species-environment relationships change when random biogeographic processes are considered?

? Null Model

Family Phyllostomidae

146 species

America divided in cells of 100 by 100 km

1. Species richness of bats by geographic range overlap

Methods

Correlation estimated with adjusted R2 values

2. Empirical richness-environment correlations

Methods

Correlation estimated with adjusted R2 values

Richness correlated against three variable sets:

a. Energyb. Heterogeneityc. Seasonality

2. Empirical richness-environment correlations

Methods

2. Empirical richness-environment correlations: Uncertainty estimation by bootstrapping

Methods

R2adj.

Freq

uenc

y

0 10.5

Methods

R2adj.

Freq

uenc

y

0 10.5

2. Empirical richness-environment correlations: Uncertainty estimation by bootstrapping

a. Computer simulations in R

b. Random biogeographic processes:

1. Range spread2. Range movement3. Speciation4. Extinction

c. Constrained domain: the New World (cells of 100 by 100 kms)

3. Create a null model of the geographic diversification of Phyllostomid bats

Methods

3. Create a null model

Methods Start

3. Create a null model

Methods Start

Domain colonization

Time = 1

3. Create a null model

Methods Start

Domain colonization

Ranges too small?Range growth

Time = 1

Yes

3. Create a null model

Methods Start

Domain colonization

Ranges too small?Range growth

Range movement

Time = 1

No

Yes

3. Create a null model

Methods Start

Domain colonization

Ranges too small?Range growth

Range movement

Speciations?Speciation

Time = 1

No

Yes

Yes

3. Create a null model

Methods Start

Domain colonization

Ranges too small?Range growth

Range movement

Speciations?

Extinctions?

Speciation

Extinction

Time = 1

No

Yes

Yes

YesNo

3. Create a null model

Methods Start

Domain colonization

Ranges too small?Range growth

Range movement

Speciations?

Extinctions?

Time limit reached?

Speciation

Extinction

Time = 1

No

Yes

Yes

YesNo

No

3. Create a null model

Methods Start

Domain colonization

Ranges too small?Range growth

Range movement

Speciations?

Extinctions?

Time limit reached?

Speciation

Extinction

Time + 1

Time = 1

No

Yes

Yes

YesNo

No

No

3. Create a null model

Methods Start

Domain colonization

Ranges too small?Range growth

Range movement

Speciations?

Extinctions?

Time limit reached?

Speciation

Extinction

Time + 1

Time = 1

End

No

Yes

Yes

Yes

Yes

No

No

No

Simulation Model Richness MapsSpecies Distributions

Methods3. Create a null model of the geographic

diversification of Phyllostomid bats

Methods3. Create a null model of the geographic

diversification of Phyllostomid bats

START

Methods

1000 null modelruns

3. Create a null model of the geographic diversification of Phyllostomid bats

START

END

Methods

R2adj.

Freq

uenc

y0 10.5

3. Create a null model of the geographic diversification of Phyllostomid bats

Methods4. Test for effects of environment using null model

R2adj.

Freq

uenc

y

0 10.5

R2adj.

Freq

uenc

y

0 10.5

Significant t-test Non-Significant t-testEnvironmental effect No environmental effect

Methods5. Calculate effect size using null model

R2adj.

Freq

uenc

y

0 10.5

Richness of Phyllostomid bats in the New World is strongly associated with the environment

adj.

R2

Energy

Heterog

eneity

Seasonal

ity

Results

All three environmental predictors have a significant effect

Results

R2adj. R2

adj. R2adj.

Freq

uenc

y

Energy Heterogeneity Seasonality

However, the relative importance changes significantly when using null model

Resultsad

j. R

2

Energy

Heterog

eneity

Seasonal

ityEner

gy

Heterog

eneity

Seasonal

ity

Hed

ges’

d

Naïve null hypotheses are not appropriate for testing species-environment relationships

Expected by chance?

Observed Relationship

R2=0.513

Environmental variable Environmental variable

Ric

hnes

s

R2=0.000

Conclusions

Conclusions

Geographical evolution null models produce much more appropriate null hypotheses

Conclusions

Geographical evolution null models can significantly modify results

Jim CroninBret ElderdKyle Harms

Eve McCullochMercedes GavilanezMaria SagotLori Patrick

?

Acknowledgements